The subject matter of each of the above-noted applications and provisional application is herein incorporated in its entirety by reference thereto.
Three computer Appendices containing computer program source code for programs described herein have been submitted concurrently with the filing of this application. The Computer Appendices were converted to Computer Program Listing Compact Disk Appendices pursuant to 37 C.F.R. 1.96(c). Appendices I, II, and III are on compact disks, copy 1 and copy 2, and stored under the file name Appenixl-III.txt, 392KB, created on Apr. 6, 2001. The compact disks, copy 1 and copy 2, are identical. The information submitted on the Compact Disk is in compliance with the American Standard Code for Information Interchange (ASCII) in the IBM-PC machine format compatible with the MS-Windows operating system. The Computer Appendices, which are referred to hereafter as the xe2x80x9cCompact Disk Appendicesxe2x80x9d, are each incorporated herein by reference in its entirety.
Thus, a portion of the disclosure of this patent document contains material that is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever.
This subject matter of the invention relates to the use of prediction technology, particularly nonlinear prediction technology, for the development of medical diagnostic aids. In particular, training techniques operative on neural networks and other expert systems with inputs from patient historical information for the development of medical diagnostic tools and methods of diagnosis are provided.
Data Mining, decision support-systems and neural networks A number of computer decision-support systems have the ability to classify information and identify patterns in input data, and are particularly useful in evaluating data sets having large quantities of variables and complex interactions between variables. These computer decision systems which are collectively identified as xe2x80x9cdata miningxe2x80x9d or xe2x80x9cknowledge discovery in databasesxe2x80x9d (and herein as decision-support systems) rely on similar basic hardware components, e.g., personal computers (PCS) with a processor, internal and peripheral devices, memory devices and input/output interfaces. The distinctions between the systems arise within the software, and more fundamentally, the paradigms upon which the software is based. Paradigms that provide decision-support functions include regression methods, decision trees, discriminant analysis, pattern recognition, Bayesian decision theory, and fuzzy logic. One of the more widely used decision-support computer systems is the artificial neural network.
Artificial neural networks or xe2x80x9cneural netsxe2x80x9d are parallel information processing tools in which individual processing elements called neurons are arrayed in layers and furnished with a large number of interconnections between elements in successive layers. The functioning of the processing elements are modeled to approximate biologic neurons where the output of the processing element is determined by a typically non-linear transfer function. In a typical model for neural networks, the processing elements are arranged into an input layer for elements which receive inputs, an output layer containing one or more elements which generate an output, and one or more hidden layers of elements therebetween. The hidden layers provide the means by which non-linear problems may be solved. Within a processing element, the input signals to the element are weighted arithmetically according to a weight coefficient associated with each input. The resulting weighted sum is transformed by a selected non-linear transfer function, such as a sigmoid function, to produce an output, whose values range from 0 to 1, for each processing element. The learning process, called xe2x80x9ctrainingxe2x80x9d, is a trial-and-error process involving a series of iterative adjustments to the processing element weights so that a particular processing element provides an output which, when combined with the outputs of other processing elements, generates a result which minimizes the resulting error between the outputs of the neural network and the desired outputs as represented in the training data. Adjustment of the element weights are triggered by error signals. Training data are described as a number of training examples in which each example contains a set of input values to be presented to the neural network and an associated set of desired output values.
A common training method is backpropagation or xe2x80x9cbackpropxe2x80x9d, in which error signals are propagated backwards through the network. The error signal is used to determine how much any given element""s weight is to be changed and the error gradient, with the goal being to converge to a global minimum of the mean squared error. The path toward convergence, i.e., the gradient descent, is taken in steps, each step being an adjustment of the input weights of the processing element. The size of each step is determined by the learning rate. The slope of the gradient descent includes flat and steep regions with valleys that act as local minima, giving the false impression that convergence has been achieved, leading to an inaccurate result.
Some variants of backprop incorporate a momentum term in which a proportion of the previous weight-change value is added to the current value. This adds momentum to the algorithm""s trajectory in its gradient descent, which may prevent it from becoming xe2x80x9ctrappedxe2x80x9d in local minima. One backpropogation method which includes a momentum term is xe2x80x9cQuickpropxe2x80x9d, in which the momentum rates are adaptive. The Quickprop variation is described by Fahlman (see,xe2x80x9cFast Learning Variations on Back-Propagation: An Empirical Studyxe2x80x9d, Proceedings on the 1988 Connectionist Models Summer School, Pittsburgh, 1988, D. Touretzky, et al., eds., pp.38-51, Morgan Kaufmann, San Mateo, Calif.; and, with Lebriere, xe2x80x9cThe Cascade-Correlation Learning Architecturexe2x80x9d, Advances in Neural Information Processing Systems 2,(Denver, 1989), D. Touretzky, ed., pp. 524-32. Morgan Kaufmann, San Mateo, Calif.). The Quickprop algorithm is publicly accessible, and may be downloaded via the Internet, from the Artificial Intelligence Repository maintained by the School of Computer Science at Carnegie Mellon University. In Quickprop, a dynamic momentum rate is calculated based upon the slope of the gradient. If the slope is smaller but has the same sign as the slope following the immediately preceding weight adjustment, the weight change will accelerate. The acceleration rate is determined by the magnitude of successive differences between slope values. If the current slope is in the opposite direction from the previous slope, the weight change decelerates. The Quickprop method improves convergence speed, giving the steepest possible gradient descent, helping to prevent convergence to a local minimum.
When neural networks are trained on sufficient training data, the neural network acts as an associative memory that is able to generalize to a correct solution for sets of new input data that were not part of the training data. Neural networks have been shown to be able to operate even in the absence of complete data or in the presence of noise. It has also been observed that the performance of the network on new or test data tends to be lower than the performance on training data. The difference in the performance on test data indicates the extent to which the network was able to generalize from the training data. A neural network, however, can be retrained and thus learn from the new data, improving the overall performance of the network.
Neural nets, thus, have characteristics that make them well suited for a large number of different problems, including areas involving prediction, such as medical diagnosis.
Neural Nets and Diagnosis
In diagnosing and/or treating a patient, a physician will use patient condition, symptoms, and the results of applicable medical diagnostic tests to identify the disease state or condition of the patient. The physician must carefully determine the relevance of the symptoms and test results to the particular diagnosis and use judgement based on experience and intuition in making a particular diagnosis. Medical diagnosis involves integration of information from several sources including a medical history, a physical exam and biochemical tests. Based upon the results of the exam and tests and answers to the questions, the physician, using his or her training, experience and knowledge and expertise, formulates a diagnosis. A final diagnosis may require subsequent surgical procedures to verify or to formulate. Thus, the process of diagnosis involves a combination of decision-support, intuition and experience. The validity of a physician""s diagnosis is very dependent upon his/her experience and ability.
Because of the predictive and intuitive nature of medical diagnosis, attempts have been made to develop neural networks and other expert systems that aid in this process. The application of neural networks to medical diagnosis has been reported. For example, neural networks have been used to aid in the diagnosis of cardiovascular disorders (see, e.g., Baxt (1991) xe2x80x9cUse of an Artificial Neural Network for the Diagnosis of Myocardial Infarction,xe2x80x9d Annals of Internal Medicine 115:843; Baxt (1992) xe2x80x9cImproving the Accuracy of an Artificial Neural Network Using Multiple Differently Trained Networks,xe2x80x9d Neural Computation 4:772; Baxt (1992), xe2x80x9cAnalysis of the clinical variables that drive decision in an artificial neural network trained to identify the presence of myocardial infarction,xe2x80x9d Annals of Emergency Medicine 21:1439; and Baxt (1994) xe2x80x9cComplexity, chaos and human physiology: the justification for non-linear neural computational analysis,xe2x80x9d Cancer Letters 77:85). Other medical diagnostic applications include the use of neural networks for cancer diagnosis (see, e.g., Maclin, et al. (19910 xe2x80x9cUsing Neural Networks to Diagnose Cancerxe2x80x9d Journal of Medical Systems 15:11-9; Rogers, et al. (1994) xe2x80x9cArtificial Neural Networks for Early Detection and Diagnosis of Cancerxe2x80x9d Cancer Letters 77:79-83; Wilding, et al. (1994) xe2x80x9cApplication of Backpropogation Neural Networks to Diagnosis of Breast and Ovarian Cancerxe2x80x9d Cancer Letters 77:145-53), neuromuscular disorders (Pattichis, et al. (1995) xe2x80x9cNeural Network Models in EMG Diagnosisxe2x80x9d, IEEE Transactions on Biomedical Engineering 42:5:486-495), and chronic fatigue syndrome (Solms, et al (1996) xe2x80x9cA Neural Network Diagnostic Tool for the Chronic Fatigue Syndromexe2x80x9d, International Conference on Neural Networks, Paper No. 108). These methodologies, however, fail to address significant issues relating to the development of practical diagnostic tests for a wide range of conditions and does not address the selection of input variables.
Computerized decision-support methods other than neural networks have been reported for their applications in medical diagnostics, including knowledge-based expert systems, including MYCIN (Davis, et al., xe2x80x9cProduction Systems as a Representation for a Knowledge-based Consultation Programxe2x80x9d, Artificial Intelligence, 1977; 8:1:15-45) and its progeny TEIRESIAS, EMYCIN, PUFF, CENTAUR, VM, GUIDON, SACON, ONCOCIN and ROGET. MYCIN is an interactive program that diagnoses certain infectious diseases and prescribes anti-microbial therapy. Such knowledge-based systems contain factual knowledge and rules or other methods for using that knowledge, with all of the information and rules being pre-programmed into the system""s memory rather than the system developing its own procedure for reaching the desired result based upon input data, as in neural networks. Another computerized diagnosis method is the Bayesian network, also known as a belief or causal probabilistic network, which classifies patterns based on probability density functions from training patterns and a priori information. Bayesian decision systems are reported for uses in interpretation of mammograms for diagnosing breast cancer (Roberts, et al., xe2x80x9cMammoNet: A Bayesian Network diagnosing Breast Cancerxe2x80x9d, Midwest Artificial Intelligence and Cognitive Science Society Conference, Carbondale, Ill., April 1995) and hypertension (Blinowska, et al. (1993) xe2x80x9cDiagnosticaxe2x80x94A Bayesian Decision-Aid Systemxe2x80x94Applied to Hypertension Diagnosisxe2x80x9d, IEEE Transactions on Biomedical Engineering 40:230-35) Bayesian decision systems are somewhat limited in their reliance on linear relationships and in the number of input data points that can be handled, and may not be as well suited for decision-support involving non-linear relationships between variables. Implementation of Bayesian methods using the processing elements of a neural network can overcome some of these limitations (see, e.., Penny, et al. (1996) In xe2x80x9cNeural Networks in Clinical Medicinexe2x80x9d, Medical Decision-support, 1996; 16:4: 386-98). These methods have been used, by mimicking the physician, to diagnose disorders in which important variables are input into the system. It, however, would be of interest to use these systems to improve upon existing diagnostic procedures.
Endometriosis
Endometriosis is the growth of uterine-like tissue outside of the uterus. It affects about 15-30 percent of reproductive age women. The cause(s) of endometriosis are not known, but it may result from retrograde menstruation, the reflux of endometrial tissue and cells (menstrual debris) from the uterus into the peritoneal cavity. While retrograde menstruation is thought to occur in most or all women, it is unclear why some women develop endometriosis and others do not.
Not all women with endometriosis exhibit symptoms or suffer from the disease. The extent or severity of endometriosis does not correlate with symptoms. Some women with severe disease may be completely asymptomatic, whereas others with minimal disease may suffer from excruciating pain. Symptoms, such as infertility, pelvic pain, dysmenorrhea and past occurrence of endometriosis, that have been associated with endometriosis often occur in women who do not have endometriosis. In other instances, these symptoms may be present, and the women do have endometriosis. Although an association between these symptoms and endometriosis appears to exist, the correlation is far from perfect, the interplay with these and other factors are complex. Clinicians often perform diagnostic laparoscopies on patients whom they believe are excellent candidates for having endometriosis based a combination of the above indications. Endometriosis, however, is not present in a significant proportion of these women. Thus, endometriosis represents an example of a disease state in which a physician must draw upon experience using a complex set of information to formulate a diagnosis. The validity of the diagnosis is related to the experience and ability of the physician.
As a result, determining if a woman has endometriosis from symptoms alone has not been possible. Within the medical community, the diagnosis of endometriosis is confirmed only by direct visualization of endometrial lesions during surgery. Many physicians often impose a further restriction and demand that the suspected lesions be verified as being endometrial-like (glands and stroma) using histology on endometrial biopsied tissue. Thus, a non-invasive diagnostic test for endometriosis would be of significant benefit.
Therefore, it is an object herein to provide a non-invasive diagnostic aid for endometriosis. It is also an object herein to provide methods to select important variables to be used in decision-support systems to aid in diagnosis of endometriosis and other disorders and conditions. It is also an object herein to identify new variables, identify new biochemical tests and markers for diseases and to design to new diagnostic tests that improve upon existing diagnostic methodologies.
Methods using decision-support systems for the diagnosis of and for aiding in the diagnosis of diseases, disorders and other medical conditions are provided. The methods provided herein, include a method of using patient history data and identification of important variables to develop a diagnostic test; a method for identification of important selected variables; a method of designing a diagnostic test; a method of evaluating the usefulness of diagnostic test; a method of expanding clinical utility of diagnostic test, and a method of selecting a course of treatment by predicting the outcome of various possible treatments. Also provided are disease parameters or variables to aid in the diagnosis of disorders, including any disorders that are difficult to diagnose, such as endometriosis, predicting pregnancy related events, such as the likelihood of delivery within a particular time period, and other such disorders relevant to women""s health. It is understood that although women""s disorders are exemplified herein, the methods herein are applicable to any disorder or condition.
Also provided are means to use neural network training to guide the development of the tests to improve their sensitivity and specificity, and to select diagnostic tests that improve overall diagnosis of, or potential for, a disease state or medical condition. Finally, a method for evaluating the effectiveness of any given diagnostic test is described.
Thus, provided herein is a method for identifying variables or sets of variables that aid in the diagnosis of disorders or conditions. In the methods for identifying and selection of important variables and generating systems for diagnosis, patient data or information, typically patient history or clinical data are collected and variables based on this data are identified. For example, the data includes information for each patient regarding the number of pregnancies each patient has had. The extracted variable is, thus, number of pregnancies. The variables are analyzed by the decision-support systems, exemplified by neural networks, to identify important or relevant variables.
Methods are provided for developing medical diagnostic tests using computer-based decision-support systems, such as neural networks and other adaptive processing systems (collectively, xe2x80x9cdata mining toolsxe2x80x9d). The neural networks or other such systems are trained on the patient data and observations collected from a group of test patients in whom the condition is known or suspected; a subset or subsets of relevant variables are identified through the use of a decision-support system or systems, such as a neural network or a consensus of neural networks; and another set of decision-support systems is trained on the identified subset(s) to produce a consensus decision-support system based test, such as a neural net-based test for the condition. The use of consensus systems, such as consensus neural networks, minimizes the negative effects of local minima in decision-support systems, such as neural network-based systems, thereby improving the accuracy of the system.
Also, to refine or improve performance, the patient data can be augmented by increasing the number of patients used. Also biochemical test data and other data may be included as part of additional examples or by using the data as additional variables prior to the variable selection process.
The resulting systems are used as an aid in diagnosis. In addition, as the systems are used patient data can be stored and then used to further train the systems and to develop systems that are adapted for a particular genetic population. This inputting of additional data into the system may be implemented automatically or done manually. By doing so the systems continually learn and adapt to the particular environment in which they are used. The resulting systems have numerous uses in addition to diagnosis, which includes assessing the severity of a disease or disorder, predicting the outcome of a selected treatment protocol. The systems may also be used to assess the value of other data in a diagnostic procedure, such as biochemical test data and other such data, and to identify new tests that are useful for diagnosing a particular disease.
Thus, also provided are methods for improving upon existing biochemical tests, identifying relevant biochemical tests and for developing new biochemical tests to aid in diagnosis of disorders and conditions. These methods involve assessing the effect of a particular test or a potential new test on the performance of the decision-support system based test. If addition of information from the test improves performance, such test will have relevance in diagnosis.
The disorders and conditions that are of particular interest herein and to which the methods herein may be readily applied, are gynecological conditions and other conditions that impact on fertility, including but not limited to endometriosis, infertility, prediction of pregnancy-related events, such as the likelihood of delivery within a particular time period, and pre-eclampsia. It is understood, however, that the methods herein are applicable to any disorder or condition.
The methods are exemplified with reference to neural networks, however, it is understood that other data mining tools, such as expert systems, fuzzy logic, decision trees, and other statistical decision-support systems which are generally non-linear, may be used. Although the variables provided herein are intended to be used with decision-support systems, once the variables are identified, then a person, typically a physician, armed with knowledge the important variables can use them to aid in diagnosis in the absence of a decision-support system or using a less complex linear system of analysis.
As shown herein, variables or combinations thereof that heretofore were not known to be important in aiding in diagnosis are identified. In addition, patient history data, without supplementing biochemical test data, can be used to diagnose or aid in diagnosing a disorder or condition when used with the decision-support systems, such as the neural nets provided herein. Furthermore, the accuracy of the diagnosis with or without biochemical data may be sufficient to obviate the need for invasive surgical diagnostic procedures.
Also provided herein is a method of identifying and expanding clinical utility of diagnostic test. The results of a particular test, particular one that had heretofore not been considered of clinical utility with respect to the disorder or condition of interest, are combined with the variables and used with the decision-support system, such as a neural net. If the performance, the ability to correctly diagnose a disorder, of the system is improved by addition of the results of the test, then the test will have clinical utility or a new utility.
Similarly, the resulting systems can be used to identify new utilities for drugs or therapies and also to identify uses for particular drugs and therapies. For example, the systems can be used to select subpopulations of patients for whom a particular drug or therapy is effective. Thus, methods for expanding the indication for a drug or therapy and identifying new drugs and therapies are provided.
In specific embodiments, neural networks are employed to evaluate specific observation values and test results, to guide the development of biochemical or other diagnostic tests, and to provide the decision-support functionality for the test.
A method for identification of important variables (parameters) or sets thereof for use in the decision-support systems is also provided. This method, while exemplified herein with reference to medical diagnosis, has broad applicability in any field, such as financial analysis, in which important parameters or variables are selected from among a plurality.
In particular, a method for selecting effective combinations of variables is provided. The method involves: (1) providing a set of xe2x80x9cnxe2x80x9d candidate variables and a set of xe2x80x9cselected important variablesxe2x80x9d, which initially is empty; (2) ranking all candidate variables based on a chi square and sensitivity analysis; (3) taking the highest xe2x80x9cmxe2x80x9d ranked variables one at a time, where m is from 1 up to n, and evaluating each by training a consensus of neural nets on that variable combined with the current set of important variables; (4) selecting the best of the m variables, where the best variable is the one that gives the highest performance, and if it improves performance in comparison to the performance of the selected important variables, adding it to the xe2x80x9cselected important variablexe2x80x9d set, removing it from the candidate set and continuing processing at step (3), otherwise going to step (5); (5) if all variables on the candidate set have been evaluated, the process is complete, otherwise continue taking the next highest xe2x80x9cmxe2x80x9d ranked variables one at a time, and evaluating each by training a consensus of neural nets on that variable combined with the current set of important selected variables and performing step (4). The final set of important selected variables will contain a plurality, typically more than three to five or more variables.
In a particular embodiment, the sensitivity analysis involves: (k) determining an average observation value for each of the variables in an observation data set; (I) selecting a training example, and running the example through a decision-support system to produce an output value, designated and stored as the normal output; (m) selecting a first variable in the selected training example, replacing the observation value with the average observation value of the first variable; running the modified example in the decision-support system in the forward mode and recording the output as the modified output; (n) squaring the difference between the normal output and the modified output and accumulating it as a total for each variable, in which this total is designed the selected variable total for each variable; (o) repeat steps (m) and (n) for each variable in the example; (p) repeating steps (l)-(n) for each example in the data set, where each total for the selected variable represents the relative contribution of each variable to the determination of the decision-support system output. This total will be used to rank each variable according to its relative contribution to the determination of the decision-support system output.
As shown herein, computer-based decision-support systems such as neural networks reveal that certain input factors, which were not initially considered to be important, can influence an outcome. This ability of a neural network to reveal the relevant input factors permits its use in guiding the design of diagnostic tests. Thus a method of designing a diagnostic test, and a method of evaluating utility of diagnostic test are also provided. In each instance, the data from the test or possible test is added to the input of the decision-support system. If the results are improved when the data are included in the input, then the diagnostic test may have clinical utility. In this manner, tests that heretofore were not known to be of value in diagnosis of a particular disorder are identified, or new tests can be developed. Neural networks can add robustness to diagnostic tests by discounting the effects of spurious data points and by identifying other data points that might be substituted, if any.
Networks are trained on one set of variables and then clinical data from diagnostic or biochemical test data and/or additional patient information are added to the input data. Any variable that improves the results compared to their absence is (are) selected. As a result, particular tests that heretofore were of unknown value in diagnosing a particular disorder can be shown to have relevance. For example, the presence or absence of particular spots on a western blot of serum antibodies can be correlated with a disease state. Based on the identity of particular spots (i.e., antigens) new diagnostic tests can be developed.
An example of the application of the prediction technology to aid in the diagnosis of disease and more particularly the use of neural network techniques with inputs from various information sources to aid in the diagnosis of the disease endometriosis is provided. A trained set of neural networks operative according to a consensus of networks in a computer system is employed to evaluate specific clinical associations, for example obtained by survey, some of which may not generally be associated with a disease condition. This is demonstrated with an exemplary disease condition endometriosis, and factors used to aid in the diagnosis of endometriosis are provided. The neural network training is based on correlations between answers to questions furnished by physicians of a significant number of clinical patients whose disease condition has been surgically verified, herein termed clinical data.
A plurality of factors, twelve to about sixteen, particularly a set of fourteen factors, in a specific trained neural network extracted from a collection of over forty clinical data factors have been identified as primary indicia for endometriosis. The following set of parameters: age, parity (number of births), gravidity (number of pregnancies), number abortions, smoking (packs/day), past history of endometriosis, dysmenorrhea, pelvic pain, abnormal pap/dysplasia, history pelvic surgery, medication history, pregnancy hypertension, genital warts and diabetes were identified as being significant. Other similar sets of parameters were also identified. Subsets of these variables also may be employed in diagnosing endometriosis.
In particular, any subset of the selected set of parameters, particularly the set of fourteen variables, that contain one (or more) of the following combinations of three variables can be used with a decision-support system for diagnosis of endometriosis:
a) number of births, history of endometriosis, history of pelvic surgery;
b) diabetes, pregnancy hypertension, smoking;
c) pregnancy hypertension, abnormal pap smear/dysplasia, history of endometriosis;
d) age, smoking, history of endometriosis;
e) smoking, history of endometriosis, dysmenorrhea;
f) age, diabetes, history of endometriosis;
g) pregnancy hypertension, number of births, history of endometriosis;
h) Smoking, number of births, history of endometriosis;
i) pregnancy hypertension, history endometriosis, history of pelvic surgery;
j) number of pregnancies, history of endometriosis, history of pelvic surgery;
k) number of births, abnormal PAP smear/dysplasia, history of endometriosis;
l) number of births, abnormal PAP smear/dysplasia, dysmenorrhea;
m) history of endometriosis, history of pelvic surgery, dysmenorrhea; and
n) number of pregnancies, history of endometriosis, dysmenorrhea.
Diagnostic software and exemplary neural networks that use the variables for diagnosis of endometriosis and the risk of delivery before a specified time are also provided. Software generates a clinically useful endometriosis index is provided as software that generates an index for assessing the risk are provided.
In other embodiments, the performance of a diagnostic neural network system used to test for endometriosis is enhanced by including variables based on biochemical test results from a relevant biochemical test as part of the factors (herein termed biochemical test data, which includes tests from analyses and data such as vital signs, such as pulse rate and blood pressure) used for training the network. An exemplary network that results therefrom is an augmented neural network that employs fifteen input factors, including results of the biochemical test and the fourteen clinical parameters. The set of weights of the eight augmented neural networks differ from the set of weights of the eight clinical data neural networks. The exemplified biochemical test employs an immuno-diagnostic test format, such as the ELISA diagnostic test format.
The methodology applied to endometriosis as exemplified herein can be similarly applied and used to identify factors for other disorders, including, but not limited to gynecological disorders and female-associated disorders, such as, for example, infertility, prediction of pregnancy related events, such as the likelihood of delivery within a particular time period, and pre-eclampsia. Neural networks, thus, can be trained to predict the disease state based on the identification of factors important in predicting the disease state and combining them with biochemical data.
The resulting diagnostic systems may be adapted and used not only for diagnosing the presence of a condition or disorder, but also the severity of the disorder and as an aid in selecting a course of treatment.